A Direct-Contact Photocurrent-Direction-Switching Biosensing Platform Based on In Situ Formation of CN QDs/TiO2 Nanodiscs and Double-Supported 3D DNA Walking Amplification.

Herein, a direct-contact photocurrent-direction-switching photoelectrochemical (PEC) biosensing platform for the ultrasensitive and selective detection of soluble CD146 (sCD146) is reported for the first time via in situ formation of carbon nitride quantum dots (CN QDs)/titanium dioxide (TiO2 ) nanodiscs with the double-supported 3D DNA walking amplification. In this platform, metal organic frameworks (MOFs)-derived porous TiO2 nanodiscs exhibit excellent anodic photocurrent, whereas a single-stranded auxiliary DNA (ssDNA) as biogate is absorbed onto the TiO2 nanodiscs to block active sites. Subsequently, with the help of intermediate DNAs from target sCD146-induced double-supported 3D DNA walking signal amplification, the ssDNA can leave away from the surface of TiO2 nanodiscs due to the specific hybridization with intermediate DNAs. Afterward, the successful direct contact of CN QDs on TiO2 nanodiscs by porosity and electrostatic adsorption, leads to the effective photocurrent-direction switching from anodic to cathodic photocurrent. Based on direct-contact photocurrent-direction-switching CN QDs/TiO2 nanodiscs system and double-supported 3D DNA walking signal amplification, sCD146 is detected sensitively with a wide linear range (10 fg mL-1 to 5 ng mL-1 ) and a low limit of detection (2.1 fg mL-1 ). Also, the environmentally friendly and direct-contact photocurrent-direction-switching PEC biosensor has an application prospect for cancer biomarker detection.

MiR-3180 inhibits hepatocellular carcinoma growth and metastasis by targeting lipid synthesis and uptake.

PURPOSE: Reprogrammed lipid metabolism is a hallmark of cancer that provides energy, materials, and signaling molecules for rapid cancer cell growth. Cancer cells acquire fatty acids primarily through de novo synthesis and uptake. Targeting altered lipid metabolic pathways is a promising anticancer strategy. However, their regulators have not been fully investigated, especially those targeting both synthesis and uptake. METHODS: Immunohistochemistry was performed on samples from patients with hepatocellular carcinoma (HCC) to establish the correlation between miR-3180, stearoyl-CoA desaturase-1 (SCD1), and CD36 expression, quantified via qRT-PCR and western blotting. The correlation was analyzed using a luciferase reporter assay. Cell proliferation, migration, and invasion were analyzed using CCK-8, wound healing, and transwell assays, respectively. Oil Red O staining and flow cytometry were used to detect lipids. Triglycerides and cholesterol levels were analyzed using a reagent test kit. CY3-labeled oleic acid transport was analyzed using an oleic acid transport assay. Tumor growth and metastasis were detected in vivo in a xenograft mouse model. RESULTS: MiR-3180 suppressed de novo fatty acid synthesis and uptake by targeting the key lipid synthesis enzyme SCD1 and key lipid transporter CD36. MiR-3180 suppressed HCC cell proliferation, migration, and invasion in an SCD1- and CD36-dependent manner in vitro. The mouse model demonstrated that miR-3180 inhibits HCC tumor growth and metastasis by inhibiting SCD1- and CD36-mediated de novo fatty acid synthesis and uptake. MiR-3180 expression was downregulated in HCC tissues and negatively correlated with SCD1 and CD36 levels. Patients with high miR-3180 levels showed better prognosis than those with low levels. CONCLUSIONS: Our investigation indicates that miR-3180 is a critical regulator involved in de novo fatty acid synthesis and uptake, which inhibits HCC tumor growth and metastasis by suppressing SCD1 and CD36. Therefore, miR-3180 is a novel therapeutic target and prognostic indicator for patients with HCC.

Magnetic-Nanowaxberry-Based Simultaneous Detection of Exosome and Exosomal Proteins for the Intelligent Diagnosis of Cancer.

Exosome concentration and exosomal proteins are regarded as promising cancer biomarkers. Herein, a waxberry-like magnetic bead (magnetic-nanowaxberry) which has huge surface area and strong affinity was synthesized to couple with aptamer for exosome capture and recovery. Subsequently, we developed a fluorescent assay for the sensitive, accurate, and simultaneous quantification of exosome and cancer-related exosomal proteins [epidermal growth factor receptor (EGFR) and epithelial cell adhesion molecule (EpCAM)] by using triple-colored probes to recognize EGFR and EpCAM or spontaneously anchor to the lipid bilayer. In this design, the interference of soluble proteins can be avoided due to the dual recognition strategy. Moreover, the lipid-based quantification of exosome concentration can improve the accuracy. Besides, the simultaneous detection mode can save samples and simplify the operation steps. Consequently, the assay shows high sensitivity (the limits of detection are down to 0.96 pg/mL for EGFR, 0.19 pg/mL for EpCAM, and 2.4 × 104 particles/µL for exosome), high specificity, and satisfactory accuracy. More importantly, this technique is successfully used to analyze exosomes in plasma to distinguish cancer patients from healthy individuals. To improve the diagnostic efficacy, the deep learning was used to exploit the potential pattern hidden in data obtained by the proposed method. Also, the accuracy for the intelligent diagnosis of cancer can achieve 96.0%. This study provides a new avenue for developing new biosensors for exosome analysis and intelligent disease diagnosis.

A Holistic Review of the State-of-the-Art Microfluidics for Exosome Separation: An Overview of the Current Status, Existing Obstacles, and Future Outlook.

Exosomes, a class of small extracellular vesicles (30-150 nm), are secreted by almost all types of cells into virtually all body fluids. These small vesicles are attracting increasing research attention owing to their potential for disease diagnosis and therapy. However, their inherent heterogeneity and the complexity of bio-fluids pose significant challenges for their isolation. Even the "gold standard," differential centrifugation, suffers from poor yields and is time-consuming. In this context, recent developments in microfluidic technologies have provided an ideal system for exosome extraction and these devices exhibit some fascinating properties such as high speeds, good portability, and low sample volumes. In this review, the focus is on the state-of-the-art microfluidic technologies for exosome isolation and highlight potential directions for future research and development by analyzing the challenges faced by the current strategies.

Characterization of C- and D-Class MADS-Box Genes in Orchids.

Orchids (members of the Orchidaceae family) possess unique flower morphology and adaptive reproduction strategies. Although the mechanisms underlying their perianth development have been intensively studied, the molecular basis of reproductive organ development in orchids remains largely unknown. Here, we report the identification and functional characterization of two AGAMOUS (AG)-like MADS-box genes, Dendrobium 'Orchid' AG1 (DOAG1) and DOAG2, which are putative C- and D-class genes, respectively, from the orchid Dendrobium 'Chao Praya Smile'. Both DOAG1 and DOAG2 are highly expressed in the reproductive organ, known as the column, compared to perianth organs, while DOAG2 expression gradually increases in pace with pollination-induced ovule development and is localized in ovule primordia. Ectopic expression of DOAG1, but not DOAG2, rescues floral defects in the Arabidopsis (Arabidopsis thaliana) ag-4 mutant, including reiteration of stamenoid perianth organs in inner whorls and complete loss of carpels. Downregulation of DOAG1 and DOAG2 in orchids by artificial microRNA interference using l-Met sulfoximine selection-based gene transformation systems shows that both genes are essential for specifying reproductive organ identity, yet they, exert different roles in mediating floral meristem determinacy and ovule development, respectively, in Dendrobium spp. orchids. Notably, knockdown of DOAG1 and DOAG2 also affects perianth organ development in orchids. Our findings suggest that DOAG1 and DOAG2 not only act as evolutionarily conserved C- and D-class genes, respectively, in determining reproductive organ identity, but also play hitherto unknown roles in mediating perianth organ development in orchids.

In situ detection of plasma exosomal microRNA for lung cancer diagnosis using duplex-specific nuclease and MoS2 nanosheets.

MicroRNAs (miRNAs) encapsulated in tumor-derived exosomes are becoming ideal biomarkers for the early diagnosis and prognosis of lung cancer. However, the accuracy and sensitivity are often hampered by the extraction process of exosomal miRNA using traditional methods. Herein, this study developed a fluorogenic quantitative detection method for exosomal miRNA using the fluorescence quenching properties of molybdenum disulfide (MoS2) nanosheets and the enzyme-assisted signal amplification properties of duplex-specific nuclease (DSN). First, a fluorescently-labeled nucleic acid probe was used to hybridize the target miRNA to form a DNA/RNA hybrid structure. Under the action of the DSN, the DNA single strand in the DNA/RNA hybrid strand was selectively digested into smaller oligonucleotide fragments. At the same time, the released miRNA target triggers the next reaction cycle, so as to achieve signal amplification. Then, MoS2 was used to selectively quench the fluorescence of the undigested probe leaving the fluorescent signal of the fluorescently-labeled probe fragments. The fluorometric signals for miRNA-21 had a maximum excitation/emission wavelength of 488/518 nm. Most importantly, the biosensor was then applied for the accurate quantitative detection of miRNA-21 in exosome lysates extracted from human plasma and this method was able to successfully distinguish lung cancer patients from healthy people. This biosensor provides a simple, rapid, and a highly specific quantitative method for exosomal miRNA and has promising potential to be used in the early diagnosis of lung cancer.

Adoption of improved neural network blade pattern recognition in prevention and control of corona virus disease-19 pandemic.

To explore the adoption effect of improved neural network blade pattern in corona virus disease (COVID)-19, comparative analysis is implemented. First, the following hypotheses are proposed. I: in addition to the confirmed cases and deaths, people suspected of being infected are also involved in the spread of the epidemic. II: patients who have been cured may also develop secondary infections, so it is considered that there is still a link between cured cases and the spread of the epidemic. III: only the relevant data of the previous day is used to predict the epidemic prevention and control of the next day. Then, the epidemic data from February 1st to February 15th in X province were selected as the control. The combined neural network model is used for prevention and control prediction, and the prediction results of the traditional neural network model are compared. The results show that the predictions of the daily new cases by the five neural network models have little difference with the actual value, and the trend is basically consistent. However, there are still differences in some time nodes. The errors of neural network 1 on the 6th and network 3 on the 13th are large. The accuracy of the combined neural network prediction model is high, and there is little difference between the result and the actual value at each time node. The prediction of the cumulative number of diagnoses per day of the five neural network models is also analyzed, and the results are relatively ideal. In addition, the accuracy of the combined neural network prediction model is high, and the difference between the result and the actual value at each time node is relatively small. It is found that the standard deviations of neural networks 2 and 3 are relatively high through the comparison of the deviations. The deviation means of the five models were all relatively low, and the mean deviation and standard deviation of the combined neural network model are the lowest. It is found that the accuracy of prediction on the epidemic spread in this province is good by comparing the performance of each neural network model. Regarding various indicators, the prediction accuracy of the combined neural network model is higher than that of the other four models, and its performance is also the best. Finally, the MSE of the improved neural network model is lower compared with the traditional neural network model. Moreover, with the change of learning times, the change trend of MSE is constant (P < 0.05 for all). In short, the improved neural network blade model has better performance compared with that of the traditional neural network blade model. The prediction results of the epidemic situation are accurate, and the application effect is remarkable, so the proposed model is worthy of further promotion and application in the medical field.

Size-Dependent Photothermal Conversion and Photoluminescence of Theranostic NaNdF4 Nanoparticles under Excitation of Different-Wavelength Lasers.

The narrow absorption and emission bands, long fluorescence lifetime, and excellent stability of rare earth nanoparticles (referred to as RE NPs) make them very attractive for multimodal imaging and therapy of cancer. Their narrow absorption requires the careful selection of laser wavelength to achieve the best performance, particularly for RE NPs simultaneously having photothermal and photoluminescent properties (e.g., Nd-based nanoparticles), which has not been investigated. Herein, we prepared a series of different-sized NaNdF4 nanoparticles (referred to as NNF NPs) (i.e., 4.7, 5.9, 12.8, and 15.6 nm) from ultrasmall nanoclusters and investigated their in vitro and in vivo size-dependent photothermal conversion and photoluminescence under irradiation by a 793 nm laser and an 808 nm laser, respectively. We find that all nanoparticles exhibited the better photothermal conversion performance under the irradiation of the 808 nm laser than under the 793 nm laser, of which 12.8 nm NNF NPs showed the best performance, and the temperature of their solution can be quickly increased from 30 °C to around 60 °C within 10 min under the irradiation of the 808 nm laser with a power intensity of 0.75 W/cm2. When we used the 793 nm laser to excite these NNF NPs, we found that all nanoparticles exhibited the stronger photoluminescence in the second near-infrared window (NIR-II) than under the excitation by the 808 nm laser, of which 15.6 nm NNF NPs possessed the strongest NIR-II luminescence. We then modified 12.8 nm NNF NPs with phospholipid carboxyl PEG and functionalized with RGD for actively targeted imaging of cancer. The NaNdF4@PEG@RGD nanoparticles (referred to as NNF-P-R NPs) have good biocompatibility, stability, and excellent targeting capability. The in vivo result show that 12.8 nm NNF NPs exhibited better photothermal conversion performance under the irradiation of the 808 nm laser, and stronger NIR-II fluorescence under irradiation of the 793 nm laser, which are consistent with the in vitro result. This work demonstrates the significance of selection of the proper laser wavelength for maximally taking advantage of RE nanoparticles for the diagnosis and treatment of cancer.

Fluorometric immunoassay for the simultaneous determination of the tumor markers carcinoembryonic antigen and cytokeratin 19 fragment using two kinds of CdSe/ZnS quantum dot nanobeads and magnetic beads.

A method is described for the simultaneous determination of the carcinoembryonic antigen (CEA) and cytokeratin 19 fragment (CYFRA21-1). Two kinds of CdSe/ZnS quantum dot nanobeads (QBs), with emission maxima at 530 nm (green) and 585 nm (yellow), were used as labels, and magnetic beads (MBs) for separation. The MBs were used as substrates to couple CEA and CYFRA21-1 antibody for isolating the proteins. Then, the differently colored QBs were linked to the antibodies against CEA and CYFRA21-1, respectively. Following the formation of the immunocomplex, the intensities of the green and yellow emissions were measured at the same excitation wavelength of 340 nm. The detection limits are 0.1 ng⋅mL- 1 for CEA, and of 0.2 ng⋅mL- 1 for CYFRA21-1. The recoveries from spiked serum are 92.1 - 118.1% for CEA, and from 90.8% to 115.2% for CYFRA21-1, with the relative standard deviations of 6.3 - 12.3% and 7.1 - 11.8%. The method was successfully applied to the simultaneous determination of the two proteins in human serum sample (n = 45). The results correlated well with those of the chemiluminescent enzyme immunoassay kit. Graphical AbstractSchematic presentation of the fluorescence immunoassay for the simultaneous determination of carcinoembryonic antigen (CEA) and cytokeratin 19 fragment (CYFRA21-1) based on quantum dot nanobeads (QBs) and magnetic beads (MBs) is reported. The intensities of two kinds of CdSe/ZnS QBs, with the emission maxima at 530 nm (green) and 585 nm (yellow) were measured at the same excitation wavelength of 340 nm.

The RNA-binding protein RBPMS1 represses AP-1 signaling and regulates breast cancer cell proliferation and migration.

The activator protein-1 (AP-1) transcription factor complex plays a crucial role in tumor growth and progression. However, how AP-1 transcriptional activity is repressed is not fully understood. Here, we show that RNA-binding protein with multiple splicing 1 (RBPMS1) physically and functionally interacts with AP-1 in vitro and in vivo. The RNA-recognition motif (RRM) and C-terminus of the RBPMS1 isoforms RBPMS1A and RBPMS1C, but not RBPMS1B, interacted with cFos, a member of the AP-1 family that dimerizes with cJun to stimulate AP-1 transcriptional activity. RBPMS1 did not associate with Jun proteins. RBPMS1A and RBPMS1C bound to the basic leucine zipper (bZIP) domain of cFos that mediates dimerization of AP-1 proteins. In addition, RBPMS1A-C interacted with the transcription factor Smad3, which was shown to interact with cJun and increase AP-1 transcriptional activity. RBPMS1 inhibited c-Fos or Smad3-mediated AP-1 transactivation and the expression of AP-1 target genes known to be the key regulators of cancer growth and progression, including vascular endothelial growth factor (VEGF) and cyclin D1. Mechanistically, RBPMS1 blocks the formation of the cFos/cJun or Smad3/cJun complex as well as the recruitment of cFos or Smad3 to the promoters of AP-1 target genes. In cultured cells and a mouse xenograft model, RBPMS1 inhibited the growth and migration of breast cancer cells through c-Fos or Smad3. These data suggest that RBPMS1 is a critical repressor of AP-1 signaling and RBPMS1 activation may be a useful strategy for cancer treatment.

Hematopoietic pre-B cell leukemia transcription factor interacting protein is overexpressed in gastric cancer and promotes gastric cancer cell proliferation, migration, and invasion.

Hematopoietic pre-B cell leukemia transcription factor interacting protein (HPIP) has been shown to play an important role in the development and progression of some cancers. However, the role of HPIP in gastric cancer (GC) is unclear. Here, we show that HPIP is upregulated in most GC patients and promotes GC cell proliferation, migration, and invasion. In GC patients, HPIP positively associates with tumor size and nodal metastasis, and negatively associates with tumor differentiation. Hematopoietic pre-B cell leukemia transcription factor interacting protein increases GC cell proliferation through activation of G1 /S and G2 /M cell cycle transitions, accompanied by a marked increase of the positive cell cycle regulators, including cyclin D1, cyclin A, and cyclin B1. Hematopoietic pre-B cell leukemia transcription factor interacting protein enhances GC cell migration and invasion, and modulates epithelial-mesenchymal transition, which plays a key role in cancer cell migration and invasion. These data underscore the critical role of HPIP in GC cell proliferation and progression and suggest that HPIP inhibition may be a useful therapeutic strategy for GC treatment.

Methyltransferase-like 17 physically and functionally interacts with estrogen receptors.

Estrogen exerts its physiological and pathological functions through two estrogen receptors (ERs), ERα and ERß, which act as transcription factors. Coregulators, including coactivators and corepressors, have been shown to be crucial for regulation of ER transcriptional activity. Although many coregulators have been identified to regulate activities of ERs, novel coregulators are still needed to be investigated. Here, we show that human methyltransferase-like 17 (METTL17), whose function is unknown, physically interacts with ERα and ERß, and functionally acts as a coactivator for ERs. METTL17 interacts with ER in vitro and in yeast and mammalian cells. Activation function-1 (AF1) and AF2 domains of ERs are responsible for the interaction between METTL17 and ERs. Knockdown of METTL17 reduces transcriptional activities of ERα and ERß in breast cancer cells, whereas METTL17 overexpression increases ERα and ERß transcriptional activities. Inhibition of METTL17 expression decreases mRNA and protein levels of ER target genes, including PR, cathepsin D, and pS2. Moreover, METTL17 knockdown reduces breast cancer cell growth. These results indicate that METTL17 is a novel coactivator of ERs and may play a role in breast tumorigenesis.

Attention pyramid pooling network for artificial diagnosis on pulmonary nodules.

The development of automated tools using advanced technologies like deep learning holds great promise for improving the accuracy of lung nodule classification in computed tomography (CT) imaging, ultimately reducing lung cancer mortality rates. However, lung nodules can be difficult to detect and classify, from CT images since different imaging modalities may provide varying levels of detail and clarity. Besides, the existing convolutional neural network may struggle to detect nodules that are small or located in difficult-to-detect regions of the lung. Therefore, the attention pyramid pooling network (APPN) is proposed to identify and classify lung nodules. First, a strong feature extractor, named vgg16, is used to obtain features from CT images. Then, the attention primary pyramid module is proposed by combining the attention mechanism and pyramid pooling module, which allows for the fusion of features at different scales and focuses on the most important features for nodule classification. Finally, we use the gated spatial memory technique to decode the general features, which is able to extract more accurate features for classifying lung nodules. The experimental results on the LIDC-IDRI dataset show that the APPN can achieve highly accurate and effective for classifying lung nodules, with sensitivity of 87.59%, specificity of 90.46%, accuracy of 88.47%, positive predictive value of 95.41%, negative predictive value of 76.29% and area under receiver operating characteristic curve of 0.914.

Ratiometric CRISPR/Cas12a-Triggered CHA System Coupling with the MSRE to Detect Site-Specific DNA Methylation.

The precise determination of DNA methylation at specific sites is critical for the timely detection of cancer, as DNA methylation is closely associated with the initiation and progression of cancer. Herein, a novel ratiometric fluorescence method based on the methylation-sensitive restriction enzyme (MSRE), CRISPR/Cas12a, and catalytic hairpin assembly (CHA) amplification were developed to detect site-specific methylation with high sensitivity and specificity. In detail, AciI, one of the commonly used MSREs, was employed to distinguish the methylated target from nonmethylated targets. The CRISPR/Cas12a system was utilized to recognize the site-specific target. In this process, the protospacer adjacent motif and crRNA-dependent identification, the single-base resolution of Cas12a, can effectively ensure detection specificity. The trans-cleavage ability of Cas12a can convert one target into abundant activators and can then trigger the CHA reaction, leading to the accomplishment of cascaded signal amplification. Moreover, with the structural change of the hairpin probe during CHA, two labeled dyes can be spatially separated, generating a change of the Förster resonance energy transfer signal. In general, the proposed strategy of tandem CHA after the CRISPR/Cas12a reaction not only avoids the generation of false-positive signals caused by the target-similar nucleic acid but can also improve the sensitivity. The use of ratiometric fluorescence can eradicate environmental effects by self-calibration. Consequently, the proposed approach had a detection limit of 2.02 fM. This approach could distinguish between colorectal cancer and precancerous tissue, as well as between colorectal patients and healthy people. Therefore, the developed method can serve as an excellent site-specific methylation detection tool, which is promising for biological and disease studies.

One-step and label-free ratiometric fluorescence assay for the detection of plasma exosome towards cancer diagnosis.

Exosomes are closely associated with tumor development and are regarded as viable biomarkers for cancer. Here, a ratiometric fluorescence method was proposed for the one-step and label-free detection of plasma exosomes. A bicolor streptavidin magnetic beads were specifically created with an immobilized Cy5-labeled hairpin aptamer for CD63 (Cy5-Apt) on its surface to identify exosome, and a green color SYBR Green I (SGI) embedded in the stem of Cy5-Apt to respond to exosomes. After exosome capture, the Cy5-Apt could undergo a conformational shift and release the encapsulated SGI, allowing exosome measurement based on the fluorescence ratio of Cy5 and SGI. The enrichment, separation and detection of exosomes in proposed method could be completed in one step (30 min), which is a significant improvement over previous method. Furthermore, the use of ratiometric fluorescence and magnetic separation allows for exosome enrichment and interference elimination from complex matrices, improving accuracy and sensitivity. Particularly, the assay could detect exosomes in plasma and has potential to distinguish lung cancer patients from healthy volunteers with an area under the receiver operator characteristic curve of 0.85. Besides, the study provided an efficient method for analyzing the various divisions of exosomes by merely modifying the aptamer, which holds great promise for point-of-care applications.

Overexpression of DOSOC1, an ortholog of Arabidopsis SOC1, promotes flowering in the orchid Dendrobium Chao Parya Smile.

SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1) encodes a MADS-box protein that plays an essential role in integrating multiple flowering signals to regulate the transition from vegetative to reproductive development in the model plant Arabidopsis. Although SOC1-like genes have been isolated in various angiosperms, its orthologs in Orchidaceae, one of the largest families of flowering plants, are so far unknown. To investigate the regulatory mechanisms of flowering time control in orchids, we isolated a SOC1-like gene, DOSOC1, from Dendrobium Chao Praya Smile. DOSOC1 was highly expressed in reproductive organs, including inflorescence apices, pedicels, floral buds and open flowers. Its expression significantly increased in whole plantlets during the transition from vegetative to reproductive development, which usually occurred after 8 weeks of culture in Dendrobium Chao Praya Smile. In the shoot apex at the floral transitional stage, DOSOC1 was particularly expressed in emerging floral meristems. Overexpression of DOSOC1 in wild-type Arabidopsis plants resulted in early flowering, which was coupled with the up-regulation of two other flowering promoters, AGAMOUS-LIKE 24 and LEAFY. In addition, overexpression of DOSOC1 was able partially to complement the late-flowering phenotype of Arabidopsis soc1-2 loss-of-function mutants. Furthermore, we successfully created seven 35S:DOSOC1 transgenic Dendrobium orchid lines, which consistently exhibited earlier flowering than wild-type orchids. Our results suggest that SOC1-like genes play an evolutionarily conserved role in promoting flowering in the Orchidaceae family, and that DOSOC1 isolated from Dendrobium Chao Praya Smile could serve as an important target for genetic manipulation of flowering time in orchids.

Crosstalk between GA and JA signaling mediates plant growth and defense.

Gibberellins (GAs) and jasmonates (JAs) are two types of essential phytohormones that control many aspects of plant growth and development in response to environmental and endogenous signals. GA regulates many essential plant developmental processes, while JA plays a dominant role in mediating plant response to stress. Recent studies have revealed that intensive crosstalk between GA and JA signaling is involved in both plant development and defense to biotic or abiotic stress. In particular, interaction between DELLAs and JA ZIM-domain (JAZ) proteins, which are key repressors in GA- and JA-signaling pathways, respectively, plays a key role in mediating the balance between plant growth and defense through modulating the activity of their interacting transcriptional factors in response to GA and JA signals. Here, we briefly review the recent progress in understanding the antagonistic and synergistic crosstalk between GA and JA signaling with a focus on the central role of DELLA-JAZ interaction in addressing the plant dilemma between "to grow" and "to defend" in response to various stimuli.

A diagnostic classification of lung nodules using multiple-scale residual network.

Computed tomography (CT) scans have been shown to be an effective way of improving diagnostic efficacy and reducing lung cancer mortality. However, distinguishing benign from malignant nodules in CT imaging remains challenging. This study aims to develop a multiple-scale residual network (MResNet) to automatically and precisely extract the general feature of lung nodules, and classify lung nodules based on deep learning. The MResNet aggregates the advantages of residual units and pyramid pooling module (PPM) to learn key features and extract the general feature for lung nodule classification. Specially, the MResNet uses the ResNet as a backbone network to learn contextual information and discriminate feature representation. Meanwhile, the PPM is used to fuse features under four different scales, including the coarse scale and the fine-grained scale to obtain more general lung features of the CT image. MResNet had an accuracy of 99.12%, a sensitivity of 98.64%, a specificity of 97.87%, a positive predictive value (PPV) of 99.92%, and a negative predictive value (NPV) of 97.87% in the training set. Additionally, its area under the receiver operating characteristic curve (AUC) was 0.9998 (0.99976-0.99991). MResNet's accuracy, sensitivity, specificity, PPV, NPV, and AUC in the testing set were 85.23%, 92.79%, 72.89%, 84.56%, 86.34%, and 0.9275 (0.91662-0.93833), respectively. The developed MResNet performed exceptionally well in estimating the malignancy risk of pulmonary nodules found on CT. The model has the potential to provide reliable and reproducible malignancy risk scores for clinicians and radiologists, thereby optimizing lung cancer screening management.

A review of sensors for classification and subtype discrimination of cancer: Insights into circulating tumor cells and tumor-derived extracellular vesicles.

Liquid biopsy can reflect the state of tumors in vivo non-invasively, thus providing a strong basis for the early diagnosis, individualized treatment monitoring and prognosis of tumors. Circulating tumor cells (CTCs) and tumor-derived extracellular vesicles (tdEVs) contain information-rich components, such as nucleic acids and proteins, and they are essential markers for liquid biopsies. Their capture and analysis are of great importance for the study of disease occurrence and development and, consequently, have been the subject of many reviews. However, both CTCs and tdEVs carry the biological characteristics of their original tissue, and few reviews have focused on their function in the staging and classification of cancer. In this review, we focus on state-of-the-art sensors based on the simultaneous detection of multiple biomarkers within CTCs and tdEVs, with clinical applications centered on cancer classification and subtyping. We also provide a thorough discussion of the current challenges and prospects for novel sensors with the ultimate goal of cancer classification and staging. It is hoped that these most advanced technologies will bring new insights into the clinical practice of cancer screening and diagnosis.